Autonomous Vehicle Anomaly Detection Using Unsupervised Flight Logs
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Solution Overview
Problem
Existing anomaly detection systems for autonomous vehicles, particularly drones, face challenges in exhaustively covering all potential failure modes due to their complexity and variability, as they rely on simple statistical thresholds and logical rules that cannot account for unknown future failures, especially as fleets grow and mission complexity increases.
Innovation Solution
A machine learning-based anomaly detection system that continuously trains on thousands of time series data records, including flight logs, to learn predictive models of flight dynamics, allowing for the detection of anomalies without upfront labeling of normal and anomalous data, and can transmit commands to address detected issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple statistical thresholds and logical rules are used for anomaly detection, then the system is easy to implement and operate, but it cannot exhaustively cover all potential failure modes and cannot account for unknown future failures
Solution Approach 1:
The patent replaces simple statistical thresholds and logical rules (mechanical/systematic approach) with a machine learning model that learns predictive patterns from historical flight data. The system trains a model on normal flight dynamics and uses it to detect anomalies, substituting rigid rule-based detection with adaptive intelligent detection that can identify unknown failure modes.
Solution Approach 2:
The patent transforms the anomaly detection approach by changing from fixed statistical parameters to dynamic machine learning model parameters. The system continuously trains on new flight data, allowing the detection parameters to adapt and evolve, thereby improving reliability without sacrificing operational simplicity.
2Measurement precision
If manual sifting through flight logs is performed to detect subtle abnormalities, then detection precision can be improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The patent implements an automated anomaly detection system that performs self-service by continuously monitoring flight data and automatically detecting anomalies without requiring manual intervention. The machine learning model independently analyzes flight logs, identifies subtle abnormalities, and flags missions for review, eliminating the need for time-consuming manual sifting while maintaining high detection precision.
Solution Approach 2:
The patent replaces manual human analysis of flight logs with an automated machine learning-based detection system. This substitution maintains high measurement precision by using sophisticated pattern recognition while dramatically reducing time loss by automating the detection process.
3Adaptability or versatility
If a machine learning model is trained on large amounts of flight data to detect unknown failure modes, then anomaly detection capability is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on extensive historical flight data to establish baseline patterns of normal flight dynamics. This preliminary training enables the system to adapt to various failure modes and scenarios in advance, improving versatility without requiring complex real-time adjustments during actual anomaly detection operations.
Data Source
AI summary
In some embodiments, techniques are provided for analyzing time series data to detect anomalies. In some embodiments, the time series data is processed using a machine learning model. In some embodiments, the machine learning model is trained in an unsupervised manner on large amounts of previous time series data, thus allowing highly accurate models to be created from novel data. In some embodiments, training of the machine learning model alternates between a fitting optimization and a trimming optimization to allow large amounts of training data that includes untagged anomalous records to be processed. Because a machine learning model is used, anomalies can be detected within complex systems, including but not limited to autonomous vehicles such as unmanned aerial vehicles. When anomalies are detected, commands can be transmitted to the monitored system (such as an autonomous vehicle) to respond to the anomaly.